Abstract
Objective
The gut bacterial microbiota is altered in patients with chronic kidney disease (CKD). However, the bacterial composition at each stage of CKD is unclear in these patients, including those receiving renal replacement therapy. We herein report the changes in the gut microbiota among patients with CKD.
Methods
A total of 93 individuals were recruited for the study. Seventy-three patients had stage 3-5 CKD, including those receiving renal replacement therapy (CKD group), and 20 were age- and sex-matched controls (CKD stage 1-2). The gut microbiome composition was analyzed using a 16S ribosomal RNA gene-based sequencing protocol.
Results
At the genus level, the butyrate-producing bacteria Lachnospira, Blautia, Coprococcus, Anaerostipes, and Roseburia were more abundant in the control group (linear discriminant analysis score of >3) than in the CKD group. Lachnospira was more abundant in the control group than in patients with CKD stage 3a. Compared to the control group, multiplex butyrate-producing bacteria were deficient in patients with CKD stage 3b-5D, including in patients receiving renal replacement therapy.
Conclusion
Our findings highlight the fact that the gut bacterial composition, including butyrate-producing bacteria, deteriorates from CKD stage 3b. Even after renal replacement therapy, the bacterial composition did not change.
Keywords: chronic kidney disease, gut bacterial microbiota
Introduction
The human intestinal tract contains 100 trillion microorganisms (1), and 500-1,000 microbial strains exist in each individual (2), classified into two main phyla: Firmicutes and Bacteroidetes. The gut bacterial microbiota is altered in patients with chronic kidney disease (CKD) and experimental animal models of CKD (3-6). However, the bacterial composition at each stage of CKD is unclear in patients with CKD, including those receiving renal replacement therapy.
The present study was designed to test the hypothesis that CKD in adults can lead to significant changes in the composition of gut microbial flora. Therefore, we analyzed the gut microbiome of patients with CKD stage 3-5D (withinundergoing hemodialysis) and those with CKD stage 1-2 by sequencing the 16S ribosomal RNA (rRNA) gene.
Materials and Methods
This study was reviewed and approved by the ethics committee of Fukuoka University Hospital (approval no.: H2O-07-014). Written informed consent was obtained from all patients before enrollment. The inclusion criterion for the first group was having CKD stage 3-5, diagnosed according to the Kidney Disease Improving Global Outcome guidelines (7). The exclusion criteria were secondary factors resulting in disease and treatment with corticosteroids, hormones, antibiotics, probiotics, prebiotics, and symbiotics. Patients with CKD stage 1-2 were recruited from among the Department of Internal Medicine of Fukuoka University Hospital staff as the control group.
Collection of clinical parameters
Demographic information and blood pressure data (systolic blood pressure and diastolic blood pressure) were collected. The body mass index was calculated based on the patient's height and weight. Laboratory data included the following: hemoglobin, albumin, serum potassium, serum sodium, serum phosphate, serum fasting glucose, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, urinalysis results, and serum creatinine. Creatinine levels were measured enzymatically via isotope dilution mass spectrometry. We measured the estimated glomerular filtration rate using an equation developed from the data of Japanese patients with CKD (8).
Fecal DNA extraction
DNA was extracted using the QIAamp Fast DNA Stool Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol. All DNA samples were stored at -80°C until processing.
Polymerase chain reaction amplification of bacterial 16S rRNA genes
1.Extraction of reads matching primer sequences
We extracted only the first parts of the sequencing reads using the FASTX-Toolkit (ver. 0.0.14) fastx_barcode_splitter tool to completely match the primer sequences used. We designed 36 primers for each read using the N-mix operation, considering 6 types of forward orientation and 6 types of reverse orientation. Primer sequences were removed from the extracted reads using the FASTX-Toolkit fastx_trimmer tool. Subsequently, using the sickle tool (ver. 1.33), sequences with a quality score of <20 were removed, and sequences with a length of ≤130 bases and their paired sequences were discarded.
2.Merging of reads
The reads were merged using the paired-end read-merging script FLASH (ver. 1.2.11).
3.Analyses using QIIME2
After removing chimeric and noise sequences using the dada2 plugin of QIIME2 (ver. 2021.11), representative sequences and amplicon sequence variant tables were generated. Using the feature-classifier plugin, 97% of the operational taxonomic units of the obtained representative sequences and sequences in the Greengenes database (ver. 13_8) were compared to estimate the phylogeny. Alignment and phylogenetic plugins were used to create a phylogenetic tree (9).
4.Original analyses
The export plugin tool of Qiime2 (ver. 2021.11) was used to convert the .qzv data format into viewable data.
5.Group comparison analyses
LEfSE (version 1.0.8) was used to test the relative abundances of strains between the groups (10). For this analysis, we used a linear discriminant analysis score of >3.0 and p<0.05 to identify taxonomic differences.
Results
The baseline characteristics of our study sample are reported in Table 1, structured according to CKD stage. The beta diversity of the samples was evaluated via a principal coordinates analysis (PCoA). We calculated the unweighted and weighted UniFrac distances using the PCoA tool to investigate the correlation of the microbiome among the six groups. The unweighted and weighted UniFrac distances revealed a tendency toward separation of microbiota in the samples according to the CKD stage 1-2 and CKD stage 3-5 and 5D groups (Table 2). The groups differed in terms of both taxon richness and evenness; the unweighted UniFrac was more sensitive to rare species, while the weighted UniFrac was more sensitive to common species.
Table 1.
Baseline Characteristics of the Different Groups.
| CKD stage | ||||||
|---|---|---|---|---|---|---|
| 1, 2 | 3a | 3b | 4 | 5 | 5D | |
| n | 20 | 14 | 12 | 14 | 19 | 14 |
| men, % | 69.2 | 78.6 | 58.3 | 84.6 | 76.5 | 50 |
| Age, year | 70.9 [62.3, 73.1] | 68.3 [52.2, 72.3] | 71.3 [66.4, 74.0] | 72.3 [67.9, 79.1] | 68.1 [62.0, 73.6] | 67.5 [58.2, 71.2] |
| BMI, kg/m² | 21.7 [20.3, 24.1] | 22.6 [21.1, 26.3] | 21.1 [18.3, 22.1] | 23.0 [22.4, 25.9] | 23.4 [20.7, 26.4] | 21.4 [19.5, 24.9] |
| SBP, mmHg | 139.0 [129.0, 154.0] | 136.0 [120.0, 153.0] | 139.0 [136.0, 157.0] | 133.0 [127.8, 143.0] | 141.0 [134.0, 155.0] | 142.0 [136.5, 165.5] |
| DBP, mmHg | 80.0 [77.0, 85.0] | 78.0 [76.0, 85.0] | 80.0 [79.0, 82.0] | 80.0 [73.3, 81.8] | 83.0 [79.5, 90.5] | 75.5 [70.7, 82.7] |
| Proteinuria, g/gCr | 0.6 [0.2, 2.0] | 0.7 [0.1, 2.5] | 0.5 [0.2, 1.5] | 1.4 [0.7, 4.7] | 2.8 [2.1, 4.2] | 2.6 [1.3, 3.8] |
| Hb, g/dL | 13.3 [12.4, 14.4] | 14.5 [13.5, 15.5] | 12.5 [11.5, 13.2] | 11.7 [11.2, 13.3] | 10.5 [9.7, 11.3] | 11.2 [10.8, 11.7] |
| Serum albumin, g/dL | 4.1 [3.4, 4.2] | 4.1 [3.4, 4.6] | 4.10 [3.75, 4.20] | 3.8 [3.5, 3.9] | 3.7 [3.5, 4.0] | 3.7 [3.3, 3.9] |
| sCr, mg/dL | 1.0 [0.8, 1.2] | 1.1 [1.0, 1.2] | 1.5 [1.1, 1.6] | 2.1 [1.9, 2.5] | 4.8 [4.0, 6.9] | 10.0 [8.4, 11.3] |
| eGFR, mL/min/ 1.73 m² | 64.8 [57.8, 71.8] | 51.1 [47.1, 55.0] | 36.8 [33.5, 40.9] | 23.0 [19.5, 28.0] | 9.1 [7.0, 11.9] | 3.9 [3.3, 4.7] |
| Na, mEq/L | 141.0 [140.0, 142.0] | 141.0 [139.3, 142.0] | 141.5 [140.0, 142.3] | 141.0 [140.0, 143.0] | 140.0 [138.0, 141.0] | 139.5 [137.7, 141.0] |
| K, mEq/L | 4.3 [4.2, 4.5] | 4.4 [4.2, 4.5] | 4.4 [4.2, 4.4] | 3.9 [3.7, 4.3] | 5.1 [4.2, 5.4] | 4.7 [4.4, 5.0] |
| P, mg/dL | 3.3 [3.0, 3.7] | 3.3 [3.1, 3.4] | 3.3 [3.0, 4.0] | 3.0 [3.0, 3.3] | 5.0 [4.2, 5.8] | 4.8 [4.4, 5.2] |
| Blood suger, mg/dL | 126.0 [100.0, 156.0] | 107.0 [101.0, 126.0] | 131.5 [105.5, 161.5] | 127.0 [110.0, 149.0] | 100.0 [95.8, 131.5] | 108.0 [95.5, 115.2] |
| HbA1c, % | 6.0 [5.4, 6.4] | 5.8 [5.5, 6.1] | 6.4 [5.8, 6.8] | 6.4 [6.2, 7.2] | 5.7 [5.4, 6.3] | 5.5 [5.2, 5.8] |
| TG, mg/dL | 145.0 [120.0, 197.0] | 194.5 [121.3, 211.3] | 141.5 [122.5, 170.3] | 189.5 [112.3, 253.8] | 103.0 [84.0, 156.0] | 92.0 [86.5, 133.2] |
| HDL-C, mg/dL | 53.0 [38.0, 66.5] | 47.0 [37.3, 54.5] | 65.0 [53.0, 67.0] | 53.0 [40.0, 69.0] | 51.0 [38.0, 70.0] | 50.5 [37.2, 54.7] |
| LDL-C, mg/dL | 107.0 [91.0, 127.0] | 111.0 [95.8, 130.8] | 107.0 [87.8, 130.0] | 112.5 [81.5, 154.0] | 101.0 [83.0, 116.0] | 77 [69.5, 87.0] |
| RAA inhibitors, % | 16.6 | 50 | 50 | 61.5 | 64.7 | 42.8 |
| SGLT2 inhibitors, % | 0 | 7.1 | 0 | 7.6 | 0 | 0 |
| Statins, % | 11.1 | 28.5 | 41.6 | 30.7 | 23.5 | 21.4 |
| Laxatives, % | 16.6 | 28.5 | 0 | 7.6 | 5.8 | 14.2 |
RAA inhibitors: renin-angiotensin-aldosteron inhibitors
Table 2.
Comparison of the Gut Bacterial Profiles among Patients with CKD Stage 1-2 and Those with CKD Stage 3-5, 5D.
| Unweighted UniFrac metric | |||
|---|---|---|---|
| Group 1 | Group 2 | R | p value |
| CKD stage 1, 2 | CKD stage 3a | 0.064832 | 0.051 |
| CKD stage 1, 2 | CKD stage 3b | 0.215983 | 0.003 |
| CKD stage 1, 2 | CKD stage 4 | 0.103101 | 0.037 |
| CKD stage 1, 2 | CKD stage 5 | 0.074894 | 0.009 |
| CKD stage 1, 2 | CKD stage 5D | 0.185547 | 0.004 |
Weighted UniFrac metric
| Group 1 | Group 2 | R | p value |
|---|---|---|---|
| CKD stage 1, 2 | CKD stage 3a | 0.035222 | 0.134 |
| CKD stage 1, 2 | CKD stage 3b | 0.140658 | 0.028 |
| CKD stage 1, 2 | CKD stage 4 | 0.051983 | 0.132 |
| CKD stage 1, 2 | CKD stage 5 | 0.100846 | 0.004 |
| CKD stage 1, 2 | CKD stage 5D | 0.119141 | 0.06 |
At the genus level, patients with CKD stages 3-5 and 5D had lower numbers of Anaerostipes, Blautia, Coprococcus, Lachnospira, and Roseburia and higher numbers of Parabacteroides, Clostridium, Ruminococcus, and Lactobacillus than patients with CKD stages 1-2 (Fig. 1). Patients with CKD stage 3a had reduced Lachnospira levels compared to those with CKD stage 1-2 (Fig. 2A). Patients with CKD stage 3b had reduced numbers of Anaerostipes, Lachnospira, and Roseburia compared to those with CKD stage 1-2 (Fig. 2B). Patients with CKD stage 4 had reduced numbers of Anaerostipes, Blautia, and Lachnospira compared to those with CKD stage 1-2 (Fig. 2C). Patients with CKD stage 5 had reduced Blautia, Coprococcus, and Lachnospira counts compared to those with CKD stage 1-2 (Fig. 2D) Patients with stage 5D CKD had reduced Coprococcus, Lachnospira, and Roseburia levels compared to those with stage 1-2 CKD (Fig. 2E). Patients with CKD stage 3b-5 had reduced multiplex butyric-acid bacteria levels compared to those with CKD stage 1-2.
Figure 1.
Major differentially abundant taxa within patients with CKD stage 1-2 (group 1, n=20) and those with CKD stage 3-5 (group 2, n=73) generated from a logarithmic linear discriminant analysis (LDA) and LDA score >3 determined by the effective size (LEfSe). Histogram of the LDA scores computed for differentially abundant bacterial taxa between groups 1 and 2.
Figure 2.
The major differentially abundant taxa within patients with CKD stage 1-2 (group 1, n=20), CKD stage 3a (group 3, n=14), CKD stage 3b (group 4, n=12), CKD stage 4 (group 5, n=14), CKD stage 5 (group 6, n=19), and CKD stage 5D (group 7, n=14) generated from a logarithmic linear discriminant analysis (LDA) and LDA score >3 determined by the effective size (LEfSe). Histogram of the LDA scores computed for differentially abundant bacterial taxa between groups 1 and 3 (A), between groups 1 and 4 (B), between groups 1 and 5 (C), between groups 1 and 6 (D), and between groups 1 and 7 (E).
Discussion
Uremia profoundly alters the composition of the gut microbiome (4). Because a decreased dietary fiber intake, phosphate binders, potassium binders, intestinal ischemia, acidosis, intestinal edema, and other factors can cause an imbalance in the intestinal microflora (dysbiosis), it can be expected that dysbiosis will occur when the renal function declines (11,12).
Lachnospiraceae are anaerobes belonging to the order Clostridium that convert various plant-derived polysaccharides into short-chain fatty acids (butyric acid and acetic acid). It is one of the most abundant groups of bacteria in the human gut microbiota. Bacteria belonging to this family reportedly may prevent colon cancer by producing butyrate. Butyric acid is known to induce regulatory T cells to suppress inflammation. Lachnospiraceae are reduced in the intestinal flora of patients with Crohn's disease and ulcerative colitis (13,14). This decrease in butyrate levels may be involved in the onset or persistence of inflammation. Lachnospiraceae are increased in the intestinal flora of patients with type 1 and type 2 diabetes, nonalcoholic fatty liver disease, and nonalcoholic steatohepatitis (15-18). They are known to cause diabetes when transplanted into sterile mice (17). Thus, the role of Lachnospiraceae in the human gut remains controversial.
Although there have been reports comparing the intestinal microbiota between patients with CKD stages 3-5 and 5D and those with CKD stages 1-2, there are no reports on the intestinal microbiota classified by CKD stage (3,4). Lachnospiraceae bacteria were found in healthy individuals (19). At the genus level, patients with CKD stages 3-5 and 5D had reduced Anaerostipes, Blautia, Coprococcus, Lachnospira, and Roseburia, belonging to the Lachnospiraceae family, when compared with the levels in those with CKD stages 1-2. Intestinal bacterial diversity is reduced in CKD patients (Fig. 1). Coprococcus, Lachnospira, and Roseburia were significantly reduced in patients with stage 5D CKD receiving dialysis treatment. Anaerostipes is a bacterium that was reported relatively recently, in 2002, and Blautia was reported in 2008; however, their roles have not been sufficiently elucidated. There are an increasing number of reports on the relationship between decreased levels of Blautia and diseases such as cirrhosis and hepatic encephalopathy, colorectal cancer, intestinal inflammation, breast cancer, type 1 diabetes, irritable bowel syndrome, and idiopathic inflammatory bowel disease (20).
At the genus level, patients with CKD stages 3-5 and 5D had increased numbers of Parabacteroides, Clostridium, Ruminococcus, and Lactobacillus compared to patients with CKD stages 1-2. Lactobacillus was less prevalent in patients undergoing peritoneal dialysis and among those with higher CKD stages than in patients with CKD stage 1-2 (4,21). Few large-scale studies on human intestinal bacteria have been published. The genus Clostridium contains pathogenic bacteria, and Lactobacillus is involved in dental caries. However, our analysis was only conducted at the genus level, so further analyses are required to identify the roles of specific species.
Several limitations associated with the present study warrant mention. First, the analyzed data were from a single institution, and the number of enrolled cases was relatively small. Second, because this was a cross-sectional study, the causal relationship between CKD and the gut microbiome is unclear.
Patients with CKD stage 3b-5 had reduced multiplex butyric acid bacteria. Research on the use and effect of probiotics and synbiotics in patients with CKD has focused on increasing lactic acid bacteria and bifidobacteria (22,23). Based on the present findings, we recommend increasing levels of butyric acid bacteria via dietary fiber, probiotics, prebiotics, and synbiotics for patients with CKD stage 3b-5 (with an estimated glomerular filtration rate of ≤45 mL/min/1.73 m2). More studies regarding butyric acid are needed to address the potential utility of dietary interventions, probiotics, prebiotics, and symbiotics and to avoid the administration of harmful and toxic medications.
Conclusion
Our findings highlight the fact that the gut bacterial composition, including butyric acid bacteria, deteriorates from CKD stage 3b. Even after receiving renal replacement therapy, the bacterial composition in patients with CKD did not change.
The authors state that they have no Conflict of Interest (COI).
Financial Support
This work was supported by JSPS KAKENHI (grant number JP 20K10519) and Fukuoka University (grant number 207008-000).
References
- 1. Xu J, Bjursell MK, Himrod J, et al. A genomic view of the human-Bacteroides thetaiotaomicron symbiosis. Science 299: 2074-2076, 2003. [DOI] [PubMed] [Google Scholar]
- 2. Eckburg PB, Bik EM, Bernstein CN, et al. Diversity of the human intestinal microbial flora. Science 308: 1635-1638, 2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Zhang J, Luo D, Lin Z, et al. Dysbiosis of gut microbiota in adult idiopathic membranous nephropathy with nephrotic syndrome. Microb Pathog 147: 104359, 2020. [DOI] [PubMed] [Google Scholar]
- 4. Vaziri ND, Wong J, Pahl M, et al. Chronic kidney disease alters intestinal microbial flora. Kidney Int 83: 308-315, 2013. [DOI] [PubMed] [Google Scholar]
- 5. Castillo-Rodriguez E, Fernandez-Prado R, Esteras R, et al. Impact of altered intestinal microbiota on chronic kidney disease progression. Toxins (Basel) 10: 300, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Hobby GP, Karaduta O, Dusio GF, Singh M, Zybailov BL, Arthur JM. Chronic kidney disease and the gut microbiome. Am J Physiol Renal Physiol 316: F1211-F1217, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Stevens PE, Levin A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 158: 825-830, 2013. [DOI] [PubMed] [Google Scholar]
- 8. Matsuo S, Imai E, Horio M, et al.; Collaborators Developing the Japanese Equation for Estimated GFR. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 53: 982-992, 2009. [DOI] [PubMed] [Google Scholar]
- 9. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461, 2010. [DOI] [PubMed] [Google Scholar]
- 10. Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol 12: R60, 2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Anders HJ, Andersen K, Stecher B. The intestinal microbiota, a leaky gut, and abnormal immunity in kidney disease. Kidney Int 83: 1010-1016, 2013. [DOI] [PubMed] [Google Scholar]
- 12. Ramezani A, Raj DS. The gut microbiome, kidney disease, and targeted interventions. J Am Soc Nephrol 25: 657-670, 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Baumgart M, Dogan B, Rishniw M, et al. Culture independent analysis of ileal mucosa reveals a selective increase in invasive Escherichia coli of novel phylogeny relative to depletion of Clostridiales in Crohn's disease involving the ileum. ISME J 1: 403-418, 2007. [DOI] [PubMed] [Google Scholar]
- 14. Frank DN, St Amand AL, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci U S A 104: 13780-13785, 2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Kostic AD, Gevers D, Siljander H, et al.; DIABIMMUNE Study Group. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17: 260-273, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Qin J, Li Y, Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490: 55-60, 2012. [DOI] [PubMed] [Google Scholar]
- 17. Kameyama K, Itoh K. Intestinal colonization by a Lachnospiraceae bacterium contributes to the development of diabetes in obese mice. Microbes Environ 29: 427-430, 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Shen F, Zheng RD, Sun XQ, Ding WJ, Wang XY, Fan JG. Gut microbiota dysbiosis in patients with nonalcoholic fatty liver disease. Hepatobiliary Pancreat Dis Int 16: 375-381, 2017. [DOI] [PubMed] [Google Scholar]
- 19. Barros AF, Borges NA, Ferreira DC, et al. Is there interaction between gut microbial profile and cardiovascular risk in chronic kidney disease patients? Future Microbiol 10: 517-526, 2015. [DOI] [PubMed] [Google Scholar]
- 20. Hamajima H, Matsunaga H, Fujikawa A, et al. Japanese traditional dietary fungus koji Aspergillus oryzae functions as a prebiotic for Blautia coccoides through glycosylceramide: Japanese dietary fungus koji is a new prebiotic. Springerplus 5: 1321, 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Wang IK, Lai HC, Yu CJ, et al. Real-time PCR analysis of the intestinal microbiotas in peritoneal dialysis patients. Appl Environ Microbiol 78: 1107-1112, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Soleimani A, Zarrati Mojarrad M, Bahmani F, et al. Probiotic supplementation in diabetic hemodialysis patients has beneficial metabolic effects. Kidney Int 91: 435-442, 2017. [DOI] [PubMed] [Google Scholar]
- 23. Ranganathan N, Ranganathan P, Friedman EA, et al. Pilot study of probiotic dietary supplementation for promoting healthy kidney function in patients with chronic kidney disease. Adv Ther 27: 634-647, 2010. [DOI] [PubMed] [Google Scholar]